A NEW CNN-BASED METHOD FOR MULTI DIRECTIONAL CAR LICENSE PLATE DETECTION

Abstract

This paper presents a novel convolutional neural network (CNN) -based method for high-accuracy real-time car license plate detection. Many contemporary methods for car license plate detection are reasonably effective under the specific conditions or strong assumptions only. However, they exhibit poor performance when the assessed car license plate images have a degree of rotation, as a result of manual capture by traffic police or deviation of the camera. Therefore, we propose the CNN-based MD-YOLO framework for multi-directional car license plate detection. Using accurate rotation angle prediction and a fast intersection-over-union evaluation strategy, our proposed method can elegantly manage rotational problems in realtime scenarios. A series of experiments have been carried out to establish that the proposed method outperforms over other existing state-of-the-art methods in terms of better accuracy and lower computational cost.

Existing System

Color, edge and morphology method.

Proposed System

Inspired by the “you only look once” (YOLO) framework, we propose a CNN-based method that can manage the multi-directional problem reasonably well. We refer to this method as “MD-YOLO”. The main contributions of our work are summarized as follows: 1) We propose a novel accurate rotation angle prediction method to realize multi-directional car license plate detection; 2) To rapidly evaluate the intersection-over-union (IoU) between two rotational rectangles, we propose an approximate method, namely, the angle deviation penalty factor (ADPF); 3) To further promote the detection accuracy, we design a prepositive CNN model that is implemented before MD-YOLO, which serves to determine the “attention region” in the overall framework. The method of cascading the two models is based on prior knowledge: as the car license plates are fixed on the cars, some distance will inevitably exist between any two plates. The synergy of this concept is explained in the subsequent section; 4) The proposed method achieves state-of-the-art detection accuracy and can also be run in real time.  

Architecture

ARCHITECTURE